To alleviate this, we propose using sentence2vec [1]}, a model designed to capture the meaning of sentences. Specifically, sentence2vec learns embeddings with respect to the sentence context. It represents the sentence context as n-grams and optimizes the additive combination of the word vectors to obtain sentence embeddings. Figure REF  illustrates how class neighbors become more meaningful when we move from word2vec to sentence2vec.
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